Applying auto-adaptation filter to tracking of maneuvering target in special relative navigation

Aim: The introduction of the full paper reviews a number of papers in the open literafure and then proposes the research mentioned in the title, which is explained in sections 1 and 2. Section 1 briefs the dynamic equations and measurement equations of relative motion. The core of section 2 consists of: (1) we use statistical technique to process the normalized innovation squared to avoid false alarm better; eqs. (23), (24) and (25) are worth noticing; (2) we design the covariance matrix of auto-magnified state estimate in order to accelerate the convergence of the filter as soon as the target's maneuver is detected; eqs. (26) and (27) are worth noticing. Simulation results, presented in Figs. 2 and 3, show preliminarily that our new auto-adaptation unscented Kalman filter is indeed better for tracking maneuvering target in special relative navigation than traditional unscented Kalman filter.